Abstract
Understanding how acting bridges the emotional bond between spectators and films is essential to depict how humans interact with this rapidly growing digital medium. In recent decades, the research community made promising progress in developing facial expression recognition (FER) methods. However, no emphasis has been put in cinematographic content, which is complex by nature due to the visual techniques used to convey the desired emotions. Our work represents a step towards emotion identification in cinema through facial expressions' analysis. We presented a comprehensive overview of the most relevant datasets used for FER, highlighting problems caused by their heterogeneity and to the inexistence of a universal model of emotions. Built upon this understanding, we evaluated these datasets with a standard image classification models to analyze the feasibility of using facial expressions to determine the emotional charge of a film. To cope with the problem of lack of datasets for the scope under analysis, we demonstrated the feasibility of using a generic dataset for the training process and propose a new way to look at emotions by creating clusters of emotions based on the evidence obtained in the experiments.

Abstract
Advertisements are often inserted in multimedia content, and this is particularly relevant in TV broadcasting as they have a key financial role. In this context, the flexible and efficient processing of TV content to identify advertisement segments is highly desirable as it can benefit different actors, including the broadcaster, the contracting company, and the end user. In this context, detecting the presence of the channel logo has been seen in the state-of-the-art as a good indicator. However, the difficulty of this challenging process increases as less prior data is available to help reduce uncertainty. As a result, the literature proposals that achieve the best results typically rely on prior knowledge or pre-existent databases. This paper proposes a flexible method for processing TV broadcasting content aiming at detecting channel logos, and consequently advertising segments, without using prior data about the channel or content. The final goal is to enable stream segmentation identifying advertisement slices. The proposed method was assessed over available state-of-the-art datasets as well as additional and more challenging stream captures. Results show that the proposed method surpasses the state-of-the-art.

Abstract

Abstract
Heart failure (HF) is a multifactorial chronic syndrome with progressive increasing incidence causing a huge financial burden worldwide. Remote monitoring should, in theory, improve HF management, but given increasing morbidity and mortality, a question remains: are we monitoring it properly? Device-based home monitoring enables objective and continuous measurement of vital variables and non-invasive devices should be first choice for elderly patients. There is no shortage of literature on the subject, however, most studies were designed to monitor a single variable or class of variables that were not properly assembled and, to the best of our knowledge, there are no large randomized studies about their impact on HF patient management. To overcome this problem, we carefully selected the most critical possible HF decompensating factors to design MONITORIA, a non-invasive device for comprehensive HF home monitoring. MONITORIA stands for MOnitoring Non-Invasively To Overcome mortality Rates of heart Insufficiency on Ambulatory, and in this paper, which is part I of a series of three articles, we discuss the theoretical basis for its design. MONITORIA and its inherent follow-up strategy will optimize HF patient care as it is a promising device, which will essentially adapt innovation not to the disease but rather to the patients. (C) 2020 Sociedade Portuguesa de Cardiologia. Published by Elsevier Espana, S.L.U.

Abstract
Introduction: Heart failure (HF) represents a huge financial and economic burden worldwide. Some authors advocate that remote monitoring should be implemented to improve HF management, but given its increasing incidence, as well as its morbidity and mortality, a question still remains: are we monitoring it properly? There is no shortage of literature on home monitoring devices, however, most of them are designed to monitor an unsuitable array of variables and, to the best of our knowledge, there are no large randomized studies about their impact on morbidity/mortality of HF patients. Objective: Description of a novel monitoring device. Methods: As a solution, we designed MONITORIA (MOnitoring NonInvasively To Overcome mortality Rates of heart Insufficiency on Ambulatory). Results: This is a multimodal device that will provide real time monitoring of vital, electrophysiological, hemodynamic and chemical signs, transthoracic impedance, and physical activity levels. The device is meant to perform continuous analysis and transmission of all data. Significant alterations in a patient's variable will alert the attending physician and, in case of potentially life-threatening situations, the national emergency medical system. The MONITORIA device will, also, have a function that sends shocks or functions as a pacemaker to treat certain arrhythmias/blockades. This function can be activated the very first time the patient utilizes it, based on their risk of sudden cardiac death. Discussion/Conclusions: MONITORIA is a promising device mostly because it is included in a follow-up program that takes into account a multi-perspective feature of HF development and is based on the real world patient, adapting innovations not to the disease but rather to the patients. (C) 2021 Sociedade Portuguesa de Cardiologia. Published by Elsevier Espana, S.L.U.

Abstract
Ambient assisted living (AAL) environments represent a key concept for dealing with the inevitable problem of population-ageing. Until recently, the use of computational intensive techniques, like Machine Learning (ML) or Computer Vision (CV), were not suitable for IoT end-nodes due to their limited resources. However, recent advances in edge intelligence have somehow changed this landscape for smart environments. This paper presents an AAL scenario where the use of ML is tested in kitchen appliances recognition using CV. The goal is to help users working with those appliances through Augmented Reality (AR) on a mobile device. Seven types of kitchen appliances were selected: blender, coffee machine, fridge, water kettle, microwave, stove, and toaster. A dataset with nearly 4900 images was organized. Three different deep learning (DL) models from the literature were selected, each with a total number of parameters and architecture compatibles with their execution on mobile devices. The results obtained in the training of these models reveal precision in the test set above 95% for the model with better results. The combination of edge AI and ML opens the application of CV in smart homes and AAL without compromising mandatory requirements as system privacy or latency. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.